The Risks of Using AI in HR – HumanResource without Human

The tremendous pace of technical innovation has led to the widespread application of artificial intelligence (AI) in various domains, including human resources. Human resources processes could be made easier, more productive, and cost-effective with the help of AI(Chowdhury et al., 2023). We covered these benefits in our prior article.

 

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So, what exactly is AI?

An artificial intelligence (AI) system is a computer program that can learn and perform tasks normally associated with a human brain. This is altering the way we interact with companies, both as employees and customers (Mittelstadt, Russell and Wachter, 2019).

AI in HR

From an HR perspective, AI in HR opens up a world of possibilities for creating a virtual HR service, freeing up HR leaders and professionals to focus on other important duties. When these benefits are combined, there is an exciting opportunity to improve the employee experience, which will impact the performance of the company (Chowdhury et al., 2023).

"AI, and generative AI, in particular, is going to change the HR Tech landscape radically," recently asserted HR specialist Josh Bersin (Chowdhury et al., 2023). Among the numerous current applications of chatbots are the following:

1.       Screening job applicants

2.       Managing employee

3.       Manager inquiries

4.       Aiding with performance management and

5.       L&D, and many more (Votto et al., 2021).

The growing use of AI in HR raises a number of concerns about the risks and challenges of replacing human engagement with automated algorithms. This paper, which was partly written by AI, aims to shed light on the dangers of relying solely on AI in human resources. Additionally, it will detail the steps you take to create an inclusive workplace by balancing technology with personal interaction (Chowdhury et al., 2023). 

Bias and discrimination

It's not surprising that many human resources professionals worry that AI could normalize bigotry and intolerance. The data used to train AI systems will be objective if the algorithms are. This can only happen this way. When artificial intelligence algorithms are trained on biased or incorrect historical data, decision-making may improve. This is because the impact could increase. This may result in biased performance reviews, unequal opportunities, and prejudice when hiring diverse staff. Human resources workers can reduce this risk by identifying and fixing biases in the machine learning system's source code and data (Köchling and Wehner, 2020).

Lack of self-awareness and empathy

An effective human resources career requires the ability to connect with and understand people emotionally. This talent is essential for job success in this field. Artificial intelligence can perform repetitive tasks and handle large amounts of data, but it cannot completely understand human emotions and behaviours. AI-driven systems may make workers feel alienated, which can lower job satisfaction and dispute resolution. AI-driven systems may cause these sentiments. Keeping humans in human resources creates a more cheerful and friendly work atmosphere, ensuring that employees receive emotional support during difficult times (Das, 2024).

Employee data privacy and data protection

AI is commonly used in human resources to acquire and process huge amounts of employee data. As a result, individual and organizational data security has become extremely important. This increases the risk of data breaches, illegal access, and misuse of personal data. Inappropriately managing sensitive data increases the chance of these events. Businesses must have comprehensive data protection policies, be transparent about how they utilize artificial intelligence and data and follow industry standards. These policies are essential for enterprises (Kim and Bodie, 2020). The employer must explain the type, purpose, and use of any data acquired on them.

One-size-fits-all solutions

AI-based HR tools may be able to standardize HR difficulties. Despite its ability to increase output, it may make it harder to meet individual demands. The company considers each worker's positive and negative traits and life experiences because they are valuable assets. Relying only on artificial intelligence for staff development, performance evaluation, and organizational well-being is like saying goodbye to the human touch, which is essential for recognizing and developing potential (Kim and Bodie, 2020).

Disrupting Human Connections

Note that workplace personal relationships affect cooperation and collaboration efficiency. If artificial intelligence (AI) reduces the need for human interaction, workers may feel lonely and unmotivated. Face-to-face meetings with HR allow employees to voice issues, ask questions, and interact. Human and artificial intelligence must be used to balance emotional support and productivity (Arslan et al., 2021).

Data-driven decision-making and process speeding are two clear benefits of artificial intelligence in human resources. This is definitely a benefit. We must be cautious when implementing this shift because human resources are valuable (Arslan et al., 2021). 

The most crucial step in creating an atmosphere that values efficiency and empathy is learning how to use technology with human connection. AI can help human resource workers work more efficiently by eliminating bias, protecting user data, and maintaining personal connections. Because of this, all workers may have an equitable, secure, and welcoming workplace (Singh and Shaurya, 2021).

References

•        Arslan, A., Cooper, C., Khan, Z., Golgeci, I. and Ali, I. (2021). Artificial intelligence and human workers interaction at team level: a conceptual assessment of the challenges and potential HRM strategies. International Journal of Manpower, [online] 43(1), pp.75–88. doi:https://doi.org/10.1108/ijm-01-2021-0052.

•        Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A. and Truong, L. (2023). Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework. Human Resource Management Review, 33(1), p.100899.

•        Das, M. (2024). Learning Agility: The Journey from Self-Awareness to Self-Immersion. pp.175–195. doi:https://doi.org/10.1007/978-981-97-0503-0_9.

•        Kim, P.T. and Bodie, M.T. (2020). Artificial Intelligence and the Challenges of Workplace Discrimination and Privacy. ABA Journal of Labor and Employment Law, [online] 35, p.289. Available at: https://heinonline.org/HOL/LandingPage?handle=hein.journals/lablaw35&div=23&id=&page=.

•        Köchling, A. and Wehner, M.C. (2020). Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Business Research, [online] 13(3), pp.795–848. Available at: https://link.springer.com/article/10.1007/s40685-020-00134-w.

•        Mittelstadt, B., Russell, C. and Wachter, S. (2019). Explaining Explanations in AI. Proceedings of the Conference on Fairness, Accountability, and Transparency - FAT* ’19. [online] doi:https://doi.org/10.1145/3287560.3287574.

•        Singh, A. and Shaurya, A. (2021). Impact of Artificial Intelligence on HR practices in the UAE. Humanities and Social Sciences Communications, [online] 8(1). doi:https://doi.org/10.1057/s41599-021-00995-4.

•        Votto, A.M., Valecha, R., Najafirad, P. and Rao, H.R. (2021). Artificial Intelligence in Tactical Human Resource Management: A Systematic Literature Review. International Journal of Information Management Data Insights, [online] 1(2), p.100047. doi:https://doi.org/10.1016/j.jjimei.2021.100047.